Guest Richard, Miguel-Hurtado Oscar, Stevenage Sarah, Black Sue
School of Engineering and Digital Arts, University of Kent, UK.
School of Engineering and Digital Arts, University of Kent, UK.
J Forensic Leg Med. 2017 Nov;52:46-55. doi: 10.1016/j.jflm.2017.08.006. Epub 2017 Aug 26.
Forensic evidence often relies on a combination of accurately recorded measurements, estimated measurements from landmark data such as a subject's stature given a known measurement within an image, and inferred data. In this study a novel dataset is used to explore linkages between hand measurements, stature, leg length and stride. These three measurements replicate the type of evidence found in surveillance videos with stride being extracted from an automated gait analysis system. Through correlations and regression modelling, it is possible to generate accurate predictions of stature from hand size, leg length and stride length (and vice versa), and to predict leg and stride length from hand size with, or without, stature as an intermediary variable. The study also shows improved accuracy when a subject's sex is known a-priori. Our method and models indicate the possibility of calculating or checking relationships between a suspect's physical measurements, particularly when only one component is captured as an accurately recorded measurement.
法医证据通常依赖于准确记录的测量数据、根据图像中已知测量值(如受试者身高)等地标数据估算的测量值以及推断数据的组合。在本研究中,一个新的数据集被用于探索手部测量、身高、腿长和步幅之间的联系。这三项测量复制了监控视频中发现的证据类型,其中步幅是从自动步态分析系统中提取的。通过相关性和回归建模,可以根据手尺寸、腿长和步幅长度准确预测身高(反之亦然),并且可以在有或没有身高作为中间变量的情况下,根据手尺寸预测腿长和步幅长度。该研究还表明,当事先知道受试者的性别时,准确性会提高。我们的方法和模型表明了计算或检查嫌疑人身体测量值之间关系的可能性,特别是当只有一个组成部分作为准确记录的测量值被捕获时。